The era of discovery science for brain function was consolidated
by the launch of the 1000 Functional Connectomes Project (FCP) in December
2009. The FCP publicly released over 1300 resting state fMRI (R-fMRI)
datasets independently collected worldwide. Enthusiastically received in terms
of pageviews and downloads, the FCP represented an initial step towards addressing
the challenge of providing access to large-scale imaging samples to a broad
scientific community. In the next iteration, the International Neuroimaging
Data-sharing Initiative (INDI) was launched in October 2010 to facilitate
sharing of imaging data with corresponding phenotypic data, and to foster a
shift to prospective data sharing. Since its launch, 14 neuroimaging groups
have agreed to unrestricted, regularly scheduled sharing of datasets,
regardless of publication status; 295 datasets have been shared to date. In
addition, 1009 previously published datasets were shared retrospectively,
including data from 285 children with ADHD and 491 controls through the
ADHD-200 Sample. In total, 2631 resting state fMRI datasets are currently available
for download via the FCP website at the Neuroimaging Informatics Tools and
Resources Clearinghouse (http://fcon_1000.projects.nitrc.org), a number that is growing weekly.

While embraced by users, sharing data is not trivial for the provider.
For data-sharing to succeed, shared datasets should be abundant, easily
accessible in a readily usable format and continuously monitored and
maintained. Otherwise, users will lose time, interest and trust. The easiest
roadblock to tackle was persuading researchers to openly share their data. The
movement towards unrestricted data sharing is gaining momentum, encouraged by funding
agencies. Preparing the data for successful sharing, however, required
considerable effort systematizing idiosyncrasies, as each lab typically
maintains its own data structure, naming convention, datatype (e.g., DICOM vs.
NIFTI), image orientation (e.g., ASL vs. RPI), etc. While typically easily
accomplished through an automated pipeline, we encountered numerous exceptions
often resulting in manual, dataset-specific operations. For instance, image
information such as orientation, number of acquired volumes and length of
acquisition are stored in the image header. Yet, some image operations can covertly
corrupt the header information. In the FCP, this led to a left-right
discrepancy between the anatomical and functional images included in some
datasets as well as incorrect voxel size representations in others. Indeed, as
some image idiosyncrasies remained unnoticed during data preparation, we benefited
from user feedback to identify possible erroneous datasets.

As an unfunded grassroots effort, we did not provide resources
such as cloud computing, an integrated database or advanced processing
pipelines. Since appropriate tools are abundantly available (most of them open
source and free of charge) we prioritized providing researchers with properly
organized raw data. This approach has resulted in over 25,000 downloads since
December 2009, recruiting researchers without direct access to imaging data
(e.g., statisticians, computer scientists, mathematicians). Still, we continue
to seek to provide a more optimized data sharing experience. Thereby, we rely
on continued user input to improve our efforts, both at the front- and
back-end, reshaping the neuroimaging landscape one shared dataset at a time.